OpenRaiser/Intern-Atlas huggingface dataset:
Group then Scale: Dynamic Mixture-of-Experts Multilingual Language Model
Venue: arXiv cs.CL 2025
Year: 2025

The curse of multilinguality phenomenon is a fundamental problem of multilingual Large Language Models (LLMs), where the competition between massive languages results in inferior performance. It mainly comes from limited capacity and negative transfer between dissimilar languages. To address this issue, we propose a method to dynamically group and scale up the parameters of multilingual LLM while boosting positive transfer among similar languages. Specifically, the model is first tuned on monolingual corpus to determine the parameter deviation in each layer and quantify the similarity between languages. Layers with more deviations are extended to mixture-of-experts layers to reduce competition between languages, where one expert module serves one group of similar languages. Experimental results on 18 to 128 languages show that our method reduces the negative transfer between languages and significantly boosts multilingual performance with fewer parameters. Such language group specialization on experts benefits the new language adaptation and reduces the inference on the previous multilingual knowledge learned.
LARGO: Low-Rank Regulated Gradient Projection for Robust Parameter Efficient Fine-Tuning
Venue: arXiv cs.CV 2025
Year: 2025

The advent of parameter-efficient fine-tuning methods has significantly reduced the computational burden of adapting large-scale pretrained models to diverse downstream tasks. However, existing approaches often struggle to achieve robust performance under domain shifts while maintaining computational efficiency. To address this challenge, we propose Low-rAnk Regulated Gradient Projection (LARGO) algorithm that integrates dynamic constraints into low-rank adaptation methods. Specifically, LARGO incorporates parallel trainable gradient projections to dynamically regulate layer-wise updates, retaining the Out-Of-Distribution robustness of pretrained model while preserving inter-layer independence. Additionally, it ensures computational efficiency by mitigating the influence of gradient dependencies across layers during weight updates. Besides, through leveraging singular value decomposition of pretrained weights for structured initialization, we incorporate an SVD-based initialization strategy that minimizing deviation from pretrained knowledge. Through extensive experiments on diverse benchmarks, LARGO achieves state-of-the-art performance across in-domain and out-of-distribution scenarios, demonstrating improved robustness under domain shifts with significantly lower computational overhead compared to existing PEFT methods. The source code will be released soon.
Domain Generalization for Person Re-identification: A Survey Towards Domain-Agnostic Person Matching
Venue: arXiv cs.CV 2025
Year: 2025

Person Re-identification (ReID) aims to retrieve images of the same individual captured across non-overlapping camera views, making it a critical component of intelligent surveillance systems. Traditional ReID methods assume that the training and test domains share similar characteristics and primarily focus on learning discriminative features within a given domain. However, they often fail to generalize to unseen domains due to domain shifts caused by variations in viewpoint, background, and lighting conditions. To address this issue, Domain-Adaptive ReID (DA-ReID) methods have been proposed. These approaches incorporate unlabeled target domain data during training and improve performance by aligning feature distributions between source and target domains. Domain-Generalizable ReID (DG-ReID) tackles a more realistic and challenging setting by aiming to learn domain-invariant features without relying on any target domain data. Recent methods have explored various strategies to enhance generalization across diverse environments, but the field remains relatively underexplored. In this paper, we present a comprehensive survey of DG-ReID. We first review the architectural components of DG-ReID including the overall setting, commonly used backbone networks and multi-source input configurations. Then, we categorize and analyze domain generalization modules that explicitly aim to learn domain-invariant and identity-discriminative representations. To examine the broader applicability of these techniques, we further conduct a case study on a related task that also involves distribution shifts. Finally, we discuss recent trends, open challenges, and promising directions for future research in DG-ReID. To the best of our knowledge, this is the first systematic survey dedicated to DG-ReID.
QoS-aware Scheduling of Periodic Real-time Task Graphs on Heterogeneous Pre-occupied MECs
Venue: arXiv cs.DC 2025
Year: 2025

In latency-sensitive applications, efficient task scheduling is crucial for maintaining Quality of Service (QoS) while meeting strict timing constraints. This paper addresses the challenge of scheduling periodic tasks structured as directed acyclic graphs (DAGs) within heterogeneous, pre-occupied Mobile Edge Computing (MEC) networks. We propose a modified version of the Heterogeneous Earliest Finish Time (HEFT) algorithm designed to exploit residual processing capacity in preoccupied MEC environments. Our approach dynamically identifies idle intervals on processors to create a feasible hyperperiodic schedule that specifies an allocated virtual machine (VM), task version, and start time for each task. This scheduling strategy maximizes the aggregate QoS by optimizing task execution without disrupting the existing periodic workload, while also adhering to periodicity, precedence, and resource constraints.Experimental results demonstrate that our method achieves enhanced load balancing and resource utilization, highlighting its potential to improve performance in heterogeneous MEC infrastructures supporting real-time, periodic applications.
Deterministic Lifting Theorems for One-Way Number-on-Forehead Communication
Venue: arXiv cs.CC 2025
Year: 2025

Lifting theorems are one of the most powerful tools for proving communication lower bounds, with numerous downstream applications in proof complexity, monotone circuit lower bounds, data structures, and combinatorial optimization. However, to the best of our knowledge, prior lifting theorems have primarily focused on the two-party communication. In this paper, we propose a new lifting theorem that establishes connections between two-party communication and the Number-on-Forehead (NOF) communication model. Specifically, we present a deterministic lifting theorem that translates one-way two-party communication lower bounds into one-way NOF lower bounds. Our lifting theorem yields two applications. First, we obtain an optimal explicit separation between randomized and deterministic one-way NOF communication, even in the multi-player setting. This improves the prior square-root vs. constant separation for three players established by Kelley and Lyu (arXiv 2025). Second, we achieve optimal separations between one-round and two-round deterministic NOF communication, improving upon the previous separation of $Ω(\frac{n^{1/(k-1)}}{k^k})$ vs. $O(\log n)$ for $k$ players, as shown by Viola and Wigderson (FOCS 2007). Beyond the lifting theorems, we also apply our techniques to the disjointness problem. In particular, we provide a new proof that the deterministic one-way three-party NOF communication complexity of set disjointness is $Ω(n)$, further demonstrating the broader applicability of our methods.
MS-UMamba: An Improved Vision Mamba Unet for Fetal Abdominal Medical Image Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Recently, Mamba-based methods have become popular in medical image segmentation due to their lightweight design and long-range dependency modeling capabilities. However, current segmentation methods frequently encounter challenges in fetal ultrasound images, such as enclosed anatomical structures, blurred boundaries, and small anatomical structures. To address the need for balancing local feature extraction and global context modeling, we propose MS-UMamba, a novel hybrid convolutional-mamba model for fetal ultrasound image segmentation. Specifically, we design a visual state space block integrated with a CNN branch (SS-MCAT-SSM), which leverages Mamba's global modeling strengths and convolutional layers' local representation advantages to enhance feature learning. In addition, we also propose an efficient multi-scale feature fusion module that integrates spatial attention mechanisms, which Integrating feature information from different layers enhances the feature representation ability of the model. Finally, we conduct extensive experiments on a non-public dataset, experimental results demonstrate that MS-UMamba model has excellent performance in segmentation performance.
Language Surgery in Multilingual Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) have demonstrated remarkable generalization capabilities across tasks and languages, revolutionizing natural language processing. This paper investigates the naturally emerging representation alignment in LLMs, particularly in the middle layers, and its implications for disentangling language-specific and language-agnostic information. We empirically confirm the existence of this alignment, analyze its behavior in comparison to explicitly designed alignment models, and demonstrate its potential for language-specific manipulation without semantic degradation. Building on these findings, we propose Inference-Time Language Control (ITLC), a novel method that leverages latent injection to enable precise cross-lingual language control and mitigate language confusion in LLMs. Our experiments highlight ITLC's strong cross-lingual control capabilities while preserving semantic integrity in target languages. Furthermore, we demonstrate its effectiveness in alleviating the cross-lingual language confusion problem, which persists even in current large-scale LLMs, leading to inconsistent language generation. This work advances our understanding of representation alignment in LLMs and introduces a practical solution for enhancing their monolingual and cross-lingual performance.
Learning Best Paths in Quantum Networks
Venue: arXiv cs.NI 2025
Year: 2025

Quantum networks (QNs) transmit delicate quantum information across noisy quantum channels. Crucial applications, like quantum key distribution (QKD) and distributed quantum computation (DQC), rely on efficient quantum information transmission. Learning the best path between a pair of end nodes in a QN is key to enhancing such applications. This paper addresses learning the best path in a QN in the online learning setting. We explore two types of feedback: "link-level" and "path-level". Link-level feedback pertains to QNs with advanced quantum switches that enable link-level benchmarking. Path-level feedback, on the other hand, is associated with basic quantum switches that permit only path-level benchmarking. We introduce two online learning algorithms, BeQuP-Link and BeQuP-Path, to identify the best path using link-level and path-level feedback, respectively. To learn the best path, BeQuP-Link benchmarks the critical links dynamically, while BeQuP-Path relies on a subroutine, transferring path-level observations to estimate link-level parameters in a batch manner. We analyze the quantum resource complexity of these algorithms and demonstrate that both can efficiently and, with high probability, determine the best path. Finally, we perform NetSquid-based simulations and validate that both algorithms accurately and efficiently identify the best path.
Towards Safety and Security Testing of Cyberphysical Power Systems by Shape Validation
Venue: arXiv cs.CR 2025
Year: 2025

The increasing complexity of cyberphysical power systems leads to larger attack surfaces to be exploited by malicious actors and a higher risk of faults through misconfiguration. We propose to meet those risks with a declarative approach to describe cyberphysical power systems and to automatically evaluate security and safety controls. We leverage Semantic Web technologies as a well-standardized framework, providing languages to specify ontologies, rules and shape constraints. We model infrastructure through an ontology which combines external ontologies, architecture and data models for sufficient expressivity and interoperability with external systems. The ontology can enrich itself through rules defined in SPARQL, allowing for the inference of knowledge that is not explicitly stated. Through the evaluation of SHACL shape constraints we can then validate the data and verify safety and security constraints. We demonstrate this concept with two use cases and illustrate how this solution can be developed further in a community-driven fashion.
Fine-Grained HDR Image Quality Assessment From Noticeably Distorted to Very High Fidelity
Venue: arXiv cs.CV 2025
Year: 2025

High dynamic range (HDR) and wide color gamut (WCG) technologies significantly improve color reproduction compared to standard dynamic range (SDR) and standard color gamuts, resulting in more accurate, richer, and more immersive images. However, HDR increases data demands, posing challenges for bandwidth efficiency and compression techniques. Advances in compression and display technologies require more precise image quality assessment, particularly in the high-fidelity range where perceptual differences are subtle. To address this gap, we introduce AIC-HDR2025, the first such HDR dataset, comprising 100 test images generated from five HDR sources, each compressed using four codecs at five compression levels. It covers the high-fidelity range, from visible distortions to compression levels below the visually lossless threshold. A subjective study was conducted using the JPEG AIC-3 test methodology, combining plain and boosted triplet comparisons. In total, 34,560 ratings were collected from 151 participants across four fully controlled labs. The results confirm that AIC-3 enables precise HDR quality estimation, with 95\% confidence intervals averaging a width of 0.27 at 1 JND. In addition, several recently proposed objective metrics were evaluated based on their correlation with subjective ratings. The dataset is publicly available.
Sense and Sensibility: What makes a social robot convincing to high-school students?
Venue: arXiv cs.RO 2025
Year: 2025

This study with 40 high-school students demonstrates the high influence of a social educational robot on students' decision-making for a set of eight true-false questions on electric circuits, for which the theory had been covered in the students' courses. The robot argued for the correct answer on six questions and the wrong on two, and 75% of the students were persuaded by the robot to perform beyond their expected capacity, positively when the robot was correct and negatively when it was wrong. Students with more experience of using large language models were even more likely to be influenced by the robot's stance -- in particular for the two easiest questions on which the robot was wrong -- suggesting that familiarity with AI can increase susceptibility to misinformation by AI. We further examined how three different levels of portrayed robot certainty, displayed using semantics, prosody and facial signals, affected how the students aligned with the robot's answer on specific questions and how convincing they perceived the robot to be on these questions. The students aligned with the robot's answers in 94.4% of the cases when the robot was portrayed as Certain, 82.6% when it was Neutral and 71.4% when it was Uncertain. The alignment was thus high for all conditions, highlighting students' general susceptibility to accept the robot's stance, but alignment in the Uncertain condition was significantly lower than in the Certain. Post-test questionnaire answers further show that students found the robot most convincing when it was portrayed as Certain. These findings highlight the need for educational robots to adjust their display of certainty based on the reliability of the information they convey, to promote students' critical thinking and reduce undue influence.
A Spatial Relationship Aware Dataset for Robotics
Venue: arXiv cs.RO 2025
Year: 2025

Robotic task planning in real-world environments requires not only object recognition but also a nuanced understanding of spatial relationships between objects. We present a spatial-relationship-aware dataset of nearly 1,000 robot-acquired indoor images, annotated with object attributes, positions, and detailed spatial relationships. Captured using a Boston Dynamics Spot robot and labelled with a custom annotation tool, the dataset reflects complex scenarios with similar or identical objects and intricate spatial arrangements. We benchmark six state-of-the-art scene-graph generation models on this dataset, analysing their inference speed and relational accuracy. Our results highlight significant differences in model performance and demonstrate that integrating explicit spatial relationships into foundation models, such as ChatGPT 4o, substantially improves their ability to generate executable, spatially-aware plans for robotics. The dataset and annotation tool are publicly available at https://github.com/PengPaulWang/SpatialAwareRobotDataset, supporting further research in spatial reasoning for robotics.
Similarity as Reward Alignment: Robust and Versatile Preference-based Reinforcement Learning
Venue: arXiv cs.LG 2025
Year: 2025

Preference-based Reinforcement Learning (PbRL) entails a variety of approaches for aligning models with human intent to alleviate the burden of reward engineering. However, most previous PbRL work has not investigated the robustness to labeler errors, inevitable with labelers who are non-experts or operate under time constraints. Additionally, PbRL algorithms often target very specific settings (e.g. pairwise ranked preferences or purely offline learning). We introduce Similarity as Reward Alignment (SARA), a simple contrastive framework that is both resilient to noisy labels and adaptable to diverse feedback formats and training paradigms. SARA learns a latent representation of preferred samples and computes rewards as similarities to the learned latent. We demonstrate strong performance compared to baselines on continuous control offline RL benchmarks. We further demonstrate SARA's versatility in applications such as trajectory filtering for downstream tasks, cross-task preference transfer, and reward shaping in online learning.
Stereotype graph: A mathematical framework of category stereotypes via graph theory
Venue: arXiv cs.DM 2025
Year: 2025

In social psychology and cognitive science, there has been much interest in studying category stereotypes. However, we still lack a consensual mathematical definition or framework, which is necessary for us to hold a deeper understanding of stereotypes in human cognition. In this paper, we use graph theory to portray category stereotypes in human cognition, based on pairs of labels having special relations. By using methods and conclusions in graph theory (including algebraic graph theory and vertex coloring) as well as strict ratiocination, we give criteria for judging the stability of a given stereotype, some of which are computationally practicable. We also define the chromatic stability index (CSI) to measure the stability of a stereotype in human cognition, as well as to provide its precise range. From the perspective of stereotype graphs and CSI, we may explain why stereotypes can easily stay in human cognition.
On the cross-correlation properties of large-size families of Costas arrays
Venue: arXiv cs.IT 2025
Year: 2025

Costas arrays have been an interesting combinatorial object for decades because of their optimal aperiodic auto-correlation properties. Meanwhile, it is interesting to find families of Costas arrays or extended arrays with small maximal cross-correlation values, since for applications in multi-user systems, the cross-interferences between different signals should also be small. The objective of this paper is to study several large-size families of Costas arrays or extended arrays, and their values of maximal crosscorrelation are partially bounded for some cases of horizontal shifts $u$ and vertical shifts $v$. Given a prime $p \geq 5$, a large-size family of Costas arrays over $\{1, \ldots, p-1\}$ is investigated, including both the exponential and logarithmic Welch Costas arrays. An upper bound on the maximal cross-correlation of this family for arbitrary $u$ and $v$ is given. We also show that the maximal cross-correlation of the family of power permutations over $\{1, \ldots, p-1\}$ for $u=0$ and $v \neq 0$ is bounded by $\frac{1}{2}+\sqrt{p-1}$. Furthermore, we give the first nontrivial upper bound on the maximal cross-correlation of the larger family including both exponential Welch Costas arrays and power permutations over $\{1, \ldots, p-1\}$ for arbitrary $u$ and $v=0$ that it equals $(p-1) / t$ where $t$ is the smallest prime divisor of $(p-1) / 2$ if p is not a safe prime and is at most $(p-1)^{\frac{1}{2}}+(p-1)^{\frac{1}{4}}+\frac{1}{2}$ otherwise.
Benchmarking Image Similarity Metrics for Novel View Synthesis Applications
Venue: arXiv cs.CV 2025
Year: 2025

Traditional image similarity metrics are ineffective at evaluating the similarity between a real image of a scene and an artificially generated version of that viewpoint [6, 9, 13, 14]. Our research evaluates the effectiveness of a new, perceptual-based similarity metric, DreamSim [2], and three popular image similarity metrics: Structural Similarity (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Learned Perceptual Image Patch Similarity (LPIPS) [18, 19] in novel view synthesis (NVS) applications. We create a corpus of artificially corrupted images to quantify the sensitivity and discriminative power of each of the image similarity metrics. These tests reveal that traditional metrics are unable to effectively differentiate between images with minor pixel-level changes and those with substantial corruption, whereas DreamSim is more robust to minor defects and can effectively evaluate the high-level similarity of the image. Additionally, our results demonstrate that DreamSim provides a more effective and useful evaluation of render quality, especially for evaluating NVS renders in real-world use cases where slight rendering corruptions are common, but do not affect image utility for human tasks.